The cheaper model that performs the same — found.
Most workflows run on whatever model was newest when they were built. Model Intelligence continuously evaluates your actual production traffic against alternative models and shows you, per workflow, where a smaller or newer model would deliver the same quality at a fraction of the cost.
The problems that pile up quietly.
You’re paying flagship-model prices for classification tasks a small model handles perfectly.
Evaluating a model switch means building a test harness nobody has time for, so switches never happen.
New cheaper models ship monthly and your stack never benefits.
What changes for your team.
Savings with proof, not promises
Each suggested switch comes with an evaluation on your own traffic: quality parity score, latency delta, and exact monthly savings — quantified from your actual usage, not vendor benchmarks.
Right-sized models everywhere
Per-workflow recommendations mean the invoice classifier runs on a small fast model while the reasoning agent keeps the flagship.
Automatic re-evaluation on releases
When providers ship new models, your workflows are re-benchmarked automatically — the savings feed stays current without you tracking release notes.
Live in minutes, not sprints.
Sample real traffic
Representative production requests are sampled per workflow (with data staying in your control).
Benchmark alternatives
Candidate models run against the sample; outputs are scored for quality parity, latency, and cost.
Recommend the switch
Where a candidate matches quality at lower cost, you get a recommendation with the full evaluation attached — switch when you’re convinced.
See it on your own AI stack.
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